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Don't Let These Examples of Big Data Failures Trip You Up Featured

Don't Let These Examples of Big Data Failures Trip You Up Steve Halama

Big data analytics is a major topic of discussion in enterprise circles these days. This has seen the rise of many projects, most of which are big in size and scope, and ambitious but the majority of which end up failing. According to a 2016 estimation by Gartner, more than 60 percent of big data projects fail. Nothing seems to have changed because many of these projects still fail to date. The question is, what is the cause of these failures? Here are some reasons why big data initiatives do not succeed:

  • Unrealistic goals and expectations

It is easy for businesses and executives to be caught up in big talk about big data projects and how it can change the fortunes of an organization. While talks can be easy compared to action, too much expectations arising from it can be a recipe for disaster as people will end up believing in the unrealistic potential of big data. Although it is good to have expectations, too much of it may end up being a disappointment. Therefore, organizations must always plan before they engage in a given big data project.

  • Leadership issues and poor project management

Strategy without leadership is a recipe of failure. With big data projects, lack of right leadership and poor project management will affect critical components of a project such as a scope, budget, quality and timing, all of which determine the success of a project. Failure of leaders to meet one or more of these critical components is the reason for the low success rate among IT projects. Big data projects are no different because they are also implemented in the same way as other IT projects and are not exempt from the failure in these areas.

  • Too much focus on technology and technique

Determining technology that is needed for a big data initiative is important. However, too much focus on these components can adversely affect concentration on other vital components such as personnel, data and cost, among others. As a project manager, ensure that there is a balance between all the components. If you are trying to experiment on technologies that you think would work well for you, then perhaps it is time you think again. You can end up wasting the company’s time and resources. Instead, try technologies that have been tried and tested.

  • Poor communication

Communication is crucial for any project. However, people always take it for granted. They see it as something that just happens through WhatsApp messages, emails or calls. As simple as it may look, lack of clear and well-defined methods of communication may lead to confusion, time wastage and irreparable damage to the team spirit. Communication should be straightforward and result-oriented since this is the only way you are going to share your mission and vision.

  • Lack of skills

Skills are everything in a project, and lack of skilled personnel to handle big data projects can be a cause for failure. Lack of skills leads to about 30 percent of failures. This causes things such as lack of understanding of data and poor project implementation. Right skills can be obtained through training, education and experience. Therefore, experienced individuals are needed to guide in the big data initiatives. Too often, companies tend to believe that in-house skills that they have will translate to success in big data. This is never the case because things keep changing along the way.

  • Lack of understanding of the real business problem

Inability to understand the real business problem is usually a result of lack of or poor communication between stakeholders and the technical personnel. Without proper knowledge of this, there is a chance that a wrong solution will be developed. A detailed business problem is needed for the right decisions to be made and the right model to be developed.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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